Now that you've got a good sense of how to "speak" R, let's use it with linear regression to make distinctive predictions. The R system has three components: a scripting language, an interactive ...

R-squared values range from 0 to 1, indicating the fit and explanatory power of a regression model. Values below 0.3 suggest weak explanatory power; above 0.7 indicate strong relationships. In finance ...

Peter Frase, opens new tab uses the controversy to rail against non-academic econobloggers, or “wonks”, who parrot the findings of academics: Zach Beauchamp, opens new tab echoes Frase’s sentiment, ...

A framework is developed for the interpretation of regression plots, including plots of the response against selected covariates, residual plots, added-variable plots, and detrended added-variable ...

We describe how to conduct a regression analysis for competing risks data. The use of an add-on package for the R statistical software is described, which allows for the estimation of the ...

Last month we explored how to model a simple relationship between two variables, such as the dependence of weight on height 1. In the more realistic scenario of dependence on several variables, we can ...

In this module, we will introduce the basic conceptual framework for statistical modeling in general, and linear statistical models in particular. In this module, we will learn how to fit linear ...

Regression analysis (or, more specifically, linear regression analysis) finds a "line of best fit" between a response variable and one or more explanatory variables. This applet allows users to look ...

I predict you'll find this logistic regression example with R to be helpful for gleaning useful information from common binary classification problems. Logistic regression is a technique used to make ...